Mapping Antarctica

Creating maps with R is usually straightforward, but representations that cross the International Date Line or that use polar projections can be tricky.

Different spatial-data providers use different conventions: some break geometries at certain longitudes (for example, cutting the Chukchi Peninsula), while others omit portions of the data. These inconsistencies can produce awkward artifacts near the poles.

In this post I fix the GISCO (European Commission) shapefile for Antarctica and produce clean orthographic maps. I walk through the manual corrections and then create a few example maps.

# Libraries
library(tidyverse)
library(sf)
library(giscoR)
library(ggrepel)
library(rmapshaper)

Fixing the geometry

First, we obtain the GISCO Antarctica polygon and transform it to an orthographic projection centered on the South Pole.

antarct <- gisco_get_countries(year = 2024, resolution = 1, country = "ATA") %>%
  select(NAME = NAME_ENGL) |>
  # Ortho proj centered in the South Pole
  st_transform(crs = "+proj=ortho +lat_0=-90 +lon_0=0")

ggplot(antarct) +
  geom_sf(fill = "lightblue")

The shapefile contains a visible “lollipop” cut that looks unnatural in an orthographic projection. I correct it manually by:

Identify the polygon that represents the main Antarctic landmass.
Convert that polygon to a sequence of coordinates (points).
Remove the small sequence of points that create the artifact.
Rebuild the polygon from the cleaned coordinates and replace the broken geometry with the corrected one.

We convert polygons to point coordinates and inspect them to find the offending sequence:

# Identify the max
ant_explode <- antarct |>
  st_cast("POLYGON")

nrow(ant_explode)
#> [1] 778

# Max polygon

ant_max <- ant_explode[which.max(st_area(ant_explode)), ]

coords <- st_coordinates(ant_max) |>
  as_tibble() |>
  # Add id for points
  mutate(np = row_number())


ggplot(coords, aes(X, Y)) +
  geom_point(size = 0.05, color = "darkblue") +
  geom_text(aes(label = np), check_overlap = TRUE) +
  coord_equal()

From the plotted indices, we can see the problematic points fall roughly in the range 8200–9200. We inspect that interval in detail to select the exact indices to remove.

test <- coords |>
  filter(np %in% seq(8200, 9200))

test |>
  ggplot(aes(X, Y)) +
  geom_point(size = 0.05, color = "darkblue") +
  geom_text(aes(label = np), check_overlap = TRUE)

Note: This cleaning is tailored to this specific shapefile and may need to be repeated for other shapefiles. The approach is straightforward but depends on the particular geometry and projection.

# Final solution after some iterations...

test |>
  filter(np %in% seq(8289, 9130)) |>
  ggplot(aes(X, Y)) +
  geom_point(color = "darkblue") +
  labs(title = "To remove")


test |>
  filter(!np %in% seq(8289, 9130)) |>
  ggplot(aes(X, Y)) +
  geom_point(color = "darkblue") +
  labs(title = "To keep")

After removing the offending points, we rebuild the polygon and reconstitute the full Antarctica shape from the corrected piece plus the remaining polygons.

# From coordinates to polygon
newpol <- coords |>
  as.data.frame() |>
  filter(!np %in% seq(8289, 9130)) |> # Removing offending points
  select(X, Y) |>
  as.matrix() |>
  list() |>
  st_polygon() |>
  st_sfc() |>
  st_set_crs(st_crs(ant_max))

ant_max_fixed <- st_sf(st_drop_geometry(ant_max), geometry = newpol)

# Regenerate initial shape
antarctica_fixed <- bind_rows(
  ant_max_fixed,
  ant_explode[-which.max(st_area(ant_explode)), ]
) |>
  group_by(NAME) |>
  summarise(m = 1) |>
  select(-m) |>
  st_make_valid()

antarctica_fixed
#> Simple feature collection with 1 feature and 1 field
#> Geometry type: MULTIPOLYGON
#> Dimension:     XY
#> Bounding box:  xmin: -2583099 ymin: -2458296 xmax: 2690846 ymax: 2233395
#> Projected CRS: +proj=ortho +lat_0=-90 +lon_0=0
#> # A tibble: 1 × 2
#>   NAME                                                                  geometry
#> * <chr>                                                       <MULTIPOLYGON [m]>
#> 1 Antarctica (((-2456385 1179033, -2456141 1178965, -2456464 1178341, -2456563 …

ggplot(antarctica_fixed) +
  geom_sf(fill = "lightblue")

Plotting examples

With the corrected shape we can produce maps. Below are a few examples based on proposed Antarctic flag designs.

Graham Bartram’s proposal (1996)

A simple rendition of Bartram’s original concept:

bbox <- st_bbox(antarctica_fixed) # For limits on the panel

antarctica_fixed |>
  ggplot() +
  geom_sf(fill = "white", color = NA) +
  theme(
    panel.background = element_rect(fill = "#009fdc"),
    panel.grid = element_blank(),
    axis.text = element_blank(),
    axis.ticks = element_blank()
  ) +
  labs(title = "Graham Bartram's proposal") +
  coord_sf(
    xlim = c(bbox[c(1, 3)]) * 1.8,
    ylim = c(bbox[c(2, 4)]) * 1.4
  )

Emblem of the Antarctic Treaty

This example uses graticules to create a concentric “bullseye” pattern around Antarctica. Generating such graticules and merging meridians requires a few extra steps to avoid small gaps near the pole.

# Need graticules
grats <- giscoR::gisco_get_countries() |>
  st_transform(st_crs(antarctica_fixed)) |>
  # Specify the cuts of the graticules
  st_graticule(
    lat = c(-80, -70, -60),
    lon = seq(-180, 180, 30),
    ndiscr = 10000,
    margin = 0.000001
  )


ggplot(grats) +
  geom_sf(color = "darkblue")

We merge meridians so the area around the South Pole is filled. st_graticule() can leave a tiny hole at the pole; we fix this by joining complementary meridians.

# Merge meridians
merid <- lapply(seq(-180, 0, 30), function(x) {
  df <- grats |>
    filter(type == "E") |>
    filter(degree %in% c(x, x + 180))

  df2 <- df |>
    st_geometry() |>
    st_cast("MULTIPOINT") |>
    st_union() |>
    st_cast("LINESTRING")

  sf_x <- st_sf(
    degree = x,
    type = "E",
    geometry = df2
  )
}) |> bind_rows()


grats_end <- merid |>
  bind_rows(grats |>
    filter(type != "E"))

We then cut and color the resulting graticules so they form the emblem-like pattern.

# Cut since some grats should be colored differently

antarctica_simp <- rmapshaper::ms_simplify(antarctica_fixed, keep = 0.005)
grats_yes <- st_intersection(grats_end, antarctica_simp)
grats_no <- st_difference(grats_end, antarctica_simp)

antarctica_simp |>
  ggplot() +
  geom_sf(fill = "white", color = NA) +
  theme(
    panel.background = element_rect(fill = "#072b5f"),
    panel.grid = element_blank(),
    axis.text = element_blank(),
    axis.ticks = element_blank()
  ) +
  geom_sf(data = grats_yes, color = "#072b5f", linewidth = 1) +
  geom_sf(data = grats_no, color = "white", linewidth = 1) +
  coord_sf(
    xlim = c(bbox[c(1, 3)]) * 1.8,
    ylim = c(bbox[c(2, 4)]) * 1.4
  ) +
  labs(title = "Emblem of the Antarctic Treaty")

Antarctica Flag Redesigned

In 2024, Graham Bartram presented a refreshed version of his flag that preserves the white Antarctic outline but fills the blue field with many tiny, colorful dots representing microplastics - even in remote regions. Because the design relies on randomness, we approximate it using the following procedure:

Sample random points across the Antarctic polygon.
Build Voronoi polygons from those points, then apply a small negative buffer to create gaps.
Randomly sample the resulting polygons to increase visual noise.
Color polygons so larger areas remain white while smaller polygons use magenta/pink tones.

# Maximum chunk of Antarctica, the one that we fixed

ant_max_fixed
#> Simple feature collection with 1 feature and 1 field
#> Geometry type: POLYGON
#> Dimension:     XY
#> Bounding box:  xmin: -2447764 ymin: -2125910 xmax: 2690846 ymax: 2233395
#> Projected CRS: +proj=ortho +lat_0=-90 +lon_0=0
#>         NAME                       geometry
#> 1 Antarctica POLYGON ((-2423737 1557908,...

set.seed(2024)
# Sample, Voronoi and negative buffer
plastics <- st_sample(ant_max_fixed, 3000) |>
  st_union() |>
  st_voronoi(envelope = st_geometry(ant_max_fixed)) |>
  st_collection_extract() |>
  st_buffer(dist = -10000)


# Keep only those properly included in the outline

toinc <- st_contains_properly(ant_max_fixed, plastics, sparse = FALSE) |>
  as.vector()

# Select random chunks
plastic_end <- plastics[toinc, ] |>
  st_as_sf() |>
  slice_sample(prop = 0.75)

ggplot(plastic_end) +
  geom_sf(fill = "darkblue")